High Desert Institute

Picture a Y-shaped logic tree descending vertically from a floating sun or node cluster, with the “roots” of the logic structure penetrating layered desert strata. Each segment of the tree could represent encapsulated modules or subgraphs, with faint glyphs representing semantic metadata. The whole structure is both architectural and ecological—anchored in a high desert basin, yet part of a planetary computing network.

🌌 Gravity: An Open Specification for Composable, Logic-Driven, Procedural Interfaces

🧽 Summary

Gravity is an open specification and architectural pattern for building self-organizing, logic-based visual interfaces. It enables both human users and LLM agents to collaboratively construct, manipulate, and explore complex workflows, logic graphs, and data flows using a consistent, composable, and declarative structure.

At its core, Gravity proposes a procedural, top-down visual language where every representation of a workflow is:

  • Deterministic (the same layout for the same logic),
  • Composable (modules can be abstracted and reused like functions),
  • Manipulable by humans and AIs alike (via the Model Context Protocol).

🧠 Philosophy

“The enemy’s gate is down.” – Ender’s Game

Just as Ender reconceptualized direction to master 3D space, Gravity reorients how we visualize logic and data. Rather than a horizontal sprawl or flat form-based systems, Gravity treats workflows as cascading flows—from cause to effect, from input to output, from abstraction to consequence.

This top-down, gravity-driven view enhances:

  • Human intuitiveness
  • Logical traceability
  • Visual consistency
  • Composability of components

However, “top-down” refers to layout convention, not necessarily the direction of execution. Flows may propagate in any direction, and backward links (e.g., for training, feedback, or correction) are supported.

🛡️ Core Concepts

1. Nodes and Graphs

  • Every workflow is a graph made of nodes and edges.

  • Nodes can represent:

    • Functions
    • Data sources/sinks
    • Operations
    • UI elements
    • Reactive observers
    • Assertions and constraints
  • Nodes support many-to-many relationships for both inputs and outputs.
  • Inputs and outputs can be of any type: scalar, string, JSON object, structured blob, function, or undefined.
  • Cycles (loops) and back-propagation paths are supported.

  • Each node contains:

    {
  "id": "uuid",
  "type": "transform|data|module|ui|logic|assert|feedback",
  "interfaces": {
    "provides": ["temperatureData", "statusFlag"],
    "consumes": ["location", "powerSignal"]
  },
  "metadata": {
    "semanticTag": "math",
    "displayName": "Sum",
    "epistemic": {"source": "sensorA", "certainty": 0.98},
    "ethical": {"bias": "low", "owner": "teamA"}
  }
}

2. Procedural Layout Engine

  • Gravity-compliant UIs use a layout engine where:

    • Visual layout is deterministic based on graph structure and metadata.
    • Layouts reflect semantic meaning: causal depth, groupings, domains.
    • Flow direction is customizable; default is top-down for readability.
    • Changes to the graph always result in predictable spatial updates.

    • Layouts can be generated by grammar rules such as:

      • Group by semantic tag
      • Align by causal phase
      • Position based on signal direction

  • Users and LLMs can trigger layout recalculations using exposed commands.

3. Encapsulation & Composition

  • Any subset of nodes can be encapsulated into a single “module” node.

  • Modules:

    • Retain external ports with preserved spatial I/O positioning
    • Are recursively explorable (“open” to view internal structure)
    • Support symbolic reference and reuse
    • Define clear namespaces and scoped local logic

4. Reactivity, Feedback & State

  • Graphs can be reactive, meaning inputs can propagate values through the system in real time.
  • Nodes may subscribe to changes and recompute automatically.
  • Feedback and back-propagation paths allow training and correction.
  • This supports simulations, learning systems, control systems, and dynamic rebalancing.
  • Future extensions include event queues, debouncing, time windows, and adaptive loops.

5. Multiple Views and Scopes

  • Graphs can be filtered and rendered from different perspectives:

    • User-specific views
    • Semantic zoom levels
    • Abstract vs. concrete representations

  • Support for scoped variables, internal aliases, and subgraph overlays.

6. Assertions, Guards, Validation & Error Handling

  • Nodes can contain assertions that must be true for propagation or evaluation to continue.

  • Validation and assertions may enforce:

    • Type or structure constraints
    • Value ranges or relationships
    • Expected invariants
    • Input structure conformity and required fields
    • Default values and type fallback behaviors
  • Useful for unit testing, validation, symbolic logic, and domain constraints. Every node must include a boilerplate validation layer that:
  • Verifies inputs before use
  • Provides clearly defined error-handling branches
  • Includes both expected and exceptional code paths

This validation layer should be:

  • Represented visually as a separate layer or toggleable view
  • Editable by users and accessible to LLMs for review, suggestion, and debugging
  • Auditable, with logs and traceability of failure modes

This follows principles adapted from high-assurance systems such as the NASA coding guidelines, ensuring reliability, predictability, and defensive design patterns.

7. Meaningful Metadata & Ontology

  • Nodes and edges may carry:

    • Epistemic tags: confidence, source, derivation history
    • Ethical tags: fairness, impact, ownership, auditability
    • Goal orientation: optimization, observation, classification

  • Gravity encourages explicit modeling of what things mean, not just what they do.

8. Human-AI Collaboration as a First-Class Feature

  • Gravity is designed to be used collaboratively with LLM agents.

  • Agents can:

    • Suggest graph rewrites
    • Preview speculative branches
    • Justify changes in natural language
    • Provide guided reasoning paths or ask clarifying questions

🧪 Interaction Model

Example: Natural-Language to Action via LLM + MCP

User: “Group the data sources at the top and collapse the math steps into a single module.”

→ LLM (via MCP):

[
  { "method": "groupBy", "params": { "attribute": "source" } },
  { "method": "encapsulateNodes", "params": { "ids": ["node3", "node4", "node5"] } },
  { "method": "applyLayout" }
]

→ The UI re-renders with grouped and encapsulated views, matching deterministic layout rules.

🔐 Security & Trust

  • Gravity specifies the use of MCP Guardian, prompt injection protections, and method whitelists for safe LLM interaction.
  • Every layout and interaction request is auditable and repeatable.

🧹 Applications

  • Visual programming environments (low/no-code)
  • Data pipeline designers
  • AI planning tools
  • Complex UI workflow builders
  • Education platforms for teaching logic and systems thinking
  • Real-time simulation systems
  • Semantic data annotation tools
  • Feedback loops and adaptive logic systems

🗋 File & Endpoint Conventions

  • /.well-known/mcp.json → Lists all available methods and schemas
  • gravity.graph.json → Stores the structure of a logic graph
  • gravity.layout.json → Stores procedural layout hints or overrides
  • gravity.module/ → Stores encapsulated subgraphs with metadata
  • gravity.views.json → Stores filtered, scoped, or user-specific views of graphs

🔮 Future Features

  • ✨ Visual versioning and diffing of logic graphs
  • ✨ Live collaboration with multi-agent AI and users
  • ✨ Visual metaphors for uncertainty, latency, or trust
  • ✨ Type hinting, validation, and inference
  • ✨ Event scheduling and time series analysis
  • ✨ Integrated symbolic reasoning, proof search, and constraint satisfaction